Learning from data with structured missingness

Mitra, Robin ORCID: https://orcid.org/0000-0001-9584-8044, McGough, Sarah F., Chakraborti, Tapabrata, Holmes, Chris, Copping, Ryan, Hagenbuch, Niels, Biedermann, Stefanie, Noonan, Jack, Lehmann, Brieuc, Shenvi, Aditi, Doan, Xuan Vinh, Leslie, David, Bianconi, Ginestra, Sanchez-Garcia, Ruben, Davies, Alisha, Mackintosh, Maxine, Andrinopoulou, Eleni-Rosalina, Basiri, Anahid, Harbron, Chris and MacArthur, Ben D. 2023. Learning from data with structured missingness. Nature Machine Intelligence 5 (1) , pp. 13-23. 10.1038/s42256-022-00596-z

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Abstract

Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Nature Research
ISSN:	2522-5839
Date of Acceptance:	21 November 2022
Last Modified:	06 May 2023 02:01
URI:	https://orca.cardiff.ac.uk/id/eprint/158506

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